Personality trait structures across three species of Macaca, using survey ratings of responses to conspecifics and humans

Comparative studies reliant on single personality surveys to rate wild primates are scarce yet remain critical for developing a holistic comparative understanding of personality. Differences in survey design, item exclusion, and factor selection impede cross-study comparisons. To address these challenges, we used consistently collected data to assess personality trait structures in wild rhesus (Macaca mulatta), bonnet (M. radiata), and long-tailed (M. fascicularis) macaques that varied in their degree of phylogenetic closeness, species-typical social styles, and anthropogenic exposure in urban or urban-rural environments. We administered 51-item personality surveys to familiar raters, and, after reliability and structure screenings, isolated 4–5 factor solutions among the species. Four consistent factors emerged: Confident, Sociable, Active, and Irritable/Equable. This latter factor had differential expression across species. Item composition of the Irritable/Equable factor was consistent with their anticipated differences in social styles, but confounded by cross-site anthropogenic variation. We also administered a 43-item survey confined to human-primate situations which paralleled our findings of social style variation, while also exhibiting variation that aligned with population differences in human density. Our findings indicate that macaque personality trait structures may be emergent outcomes of evolutionary and/or socioecological processes, but further research is needed to parse these processes’ relative contributions.


S1 Text. Comparisons of ICC cut-offs
We recognize the diversity of cut-offs that have been used in studies of primate personality.
Furthermore, though they are mathematically similar in their calculation [2][3][4], their interpretation differs [4].Thus, we also present our results informed by cut-offs with ICC(3,1).ICC(3,1) share similarities with Pearson's correlation coefficients, albeit with an emphasis on additivity not linearity [4].This is an important and relevant point as guidelines on effect sizes for correlational studies of individual differences suggest that small correlations might be reasonably expected to be 0.10.We note that our study's cut-off of ICC(3,k) ≤ 0.40 is similar to an ICC(3,1) cut-off of 0.10 (S1 Figure).Indeed, the mean ICC(3,1) of discarded items with our ICC(3,k) cut off of 0.40 were: 0.07 ±0.03sd and 0.06 ±0.04sd, for the general and human-situation ratings, respectively.
We recognize that a wide variety of studies utilize an ICC reliability cut-off of 0.00 [5][6][7].Thus, we replicated our analyses as to how our factor structure does or does not change with these two additional thresholds (ICC [3,1] 0.10 and 0.00).We present summaries of these factor models here.
An ICC(3,1) of 0.10 would yield very similar results to those reported in the manuscript, with identical or near identical factor structures and the addition of only 1-4 items in four of the six models (S3 Table ).With an ICC(3,1) threshold of 0.00, we obtained similar results for the general survey factor models across all species with an additional 2-3 items (S4 Table ).For the human-situation factor models, we obtained also similar results in the rhesus and long-tailed macaque with an ICC(3,1) threshold of 0.00.For the bonnet macaque human-situation model, however, our ICC(3,1) 0.00 threshold model explained less variation than our other humansituation models (S4 Table ).Even so, we found 3 factors with similar factor loadings (r ≥ |0.993|) and good congruence (Φ ≥ |0.99|) to our model reported in the manuscript.A fourth factor emerged relative to our reported model: Equable B H. This factor was correlated (r = 0.761), but not congruent (Φ = 0.74), with Lazy/Exploratory B H. The items that composed this factor, however, were all low in ICC(3,1): 0.066 ±0.049sd, with a range of 0.005 to 0.140; the highest loading items had ICC(3,1) estimates of 0.034 and 0.005.We report this here as a potential factor for future interest, but retain our threshold for consistency with the other species and models.We also reinforce, as reported in the manuscript, that the bonnet macaques were pronounced in having generally low reliability in the human situation, an observation that we have generated hypotheses from relevant for future research.

S3 Table.
Comparing general factor models with varying reliability cut-offs.

S2 Text. Comparing Complex Item General Models, versus Simple Models
Due to the number of complex items (i.e., items with moderate-to-heavy loadings across multiple factors), we constructed simple models to determine whether these complex items altered model structure.Following recommendations by Howard [8], simple models met the criteria that all items loaded on at least one factor ≥ 0.40, did not exceed a loading of 0.30 for remaining factors, and exceeded 0.20 between the maximum loading and the next highest loading.We excluded items one-by-one that failed this criterion and reassessed the number of factors, as detailed in the methods, with the stipulation that we did not decrease the number of factors below 4, to retain comparability between the models.We used Procrustes rotation on the factor loadings and compared congruence between the simplified and complex models.For each of the species, factors from the simple models were highly congruent (Φ ≥ 0.99) with the original models (S9 Table ), despite a reduction of 13, 15, and 19 items in the simplified bonnet, long-tailed, and rhesus macaque models, respectively.The original number of factors were retained in the bonnet macaque model.In the simplified long-tailed macaque model, however, Playful L was absent, as the two high loading items were eliminated for being complex (playful, independent).In the simplified rhesus macaque model, Equable R was absent as all the high loading items were eliminated for being complex.We prioritized retaining complex item models to increase comparability between the three species, as their removal reduced the number of items represented across species' models.

S9 Table.
Congruence between factors from the personality factor models with complex items included or excluded.Simplified factors, from models with complex items are excluded, are italicized.

S3 Text. Comparing Complex Item Human Situation Models, versus Simple Models
Following our approach with the general models, we screened the human situation factor models to remove complex items following Howard [8].We used Procrustes rotation prior to calculating congruence coefficients between the simple and complex models.For each of the species, factors from the simple models were highly congruent (Φ ≥ 0.97) with the original complex models (S3

S4 Text. Influence of Stepwise Exclusion of Items on Congruence, Human Situation Models
We sought to determine whether particular items were contributing to incongruence for items with similar item structure.We first re-obtained congruence between the factors after removing a single item.We used these leave-one-out congruence estimates to determine which item removal most greatly improved congruence.We then repeated this process of item exclusion until we attained at least poor congruence (≥0.80).
For Lazy RL H congruence was improved by removal of: insecure.We were able to improve to fair congruence with the additional removal of: cautious, understanding, and excitable, in descending order of the contribution to incongruence.
ICC (3,1) results, with unreliable values in bold: ICC(3,k) < 0.40.37 Comparing human-situation factor models with varying reliability cut-offs.Rhesus macaque factor structure for general personality ratings.Bolded items exceed a 100 loading of |0.40|.101 Long-tailed macaque factor structure for general personality ratings.Bolded items exceed a 102 loading of |0.40|.103 Bonnet macaque factor structure for general personality ratings.Bolded items exceed a loading of |0.40|.Interfactor correlation coefficients (Phi) for the personality factor models.
*After MSA and communality screening procedures described in the manuscript **The model in the manuscript is the ICC(3,k) model presented here.As the order of the factors can switch, we correlated factors based on similar item composition not order of variance explained.For example, Irritable/Equable L and Sociable L both explained 0.16 of the proportional variance and their factor order in the model often switched.S4 Table.*AfterMSA and communality screening procedures described in the manuscript **The model in the manuscript is the ICC(3,k) model presented here.As the order of the factors can switch, we correlated factors based on similar item composition not order of variance explained.†Three of the four factor loadings were highly correlated (r ≥ |0.993|) and highly congruent with our reported models (Φ ≥ |0.99|).One additional factor, however, was found which was defined by items with low reliability.See discussion above for details.S5 Table.
Items with scores < |0.40| in our fuzzy set analyses for five factors that appeared across our general factor models.See Table2for the remaining items.Rhesus macaque factor structure for human situation ratings.Bolded items exceed a loading of |0.40|.Bonnet macaque factor structure for human situation ratings.Bolded items exceed a loading of |0.40|.Interfactor correlation coefficients (Phi) for the human situation factor models.
Fig), with the exception of Exploratory L H, which still had fair congruence (Φ = 0.91) with the simplified Exploratory LH and poor congruence with Lazy L H (Φ = -0.80).These high congruence coefficients were despite a reduction of 12, 18, and 2 items in the simplified rhesus, long-tailed, and bonnet macaque models, respectively.The original number of factors were retained in the bonnet macaque models.In the simplified long-tailed macaque model, however, Congruence between factors from the human situation models with complex items included or excluded.Simplified factors, from models with complex items are excluded, are italicized.